
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "77"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 19 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 19 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459996	not_connected	100.00%	99.57%	99.62%	0.00%	-	-	nan	nan	inf	inf	nan	nan	nan	nan	0.3717	0.3259	0.3251	nan	nan
2459995	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	58.692481	25.166878	0.624796	-0.561563	4.013156	2.245171	4.325918	0.463186	0.3226	0.4971	0.2838	nan	nan
2459994	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	56.667369	22.305237	0.557749	-0.511996	4.080003	2.372629	1.906752	-0.067582	0.3221	0.5088	0.3054	nan	nan
2459991	not_connected	100.00%	99.89%	99.95%	0.00%	-	-	264.385859	264.810852	inf	inf	3074.885065	3091.427598	6322.626448	6175.430651	0.3170	0.2781	0.3339	nan	nan
2459990	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	47.388440	17.642746	0.316416	-0.346452	3.719627	5.502731	6.726533	10.952941	0.3585	0.5191	0.3070	nan	nan
2459989	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	42.659091	6.523829	0.271497	-0.087287	3.399761	1.697578	8.245590	19.086727	0.3971	0.5706	0.3557	nan	nan
2459988	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	57.802326	19.865860	0.296541	-0.223733	7.525587	6.078660	11.328214	11.695647	0.3572	0.5182	0.3077	nan	nan
2459987	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	51.962875	12.723302	0.444674	-0.276593	5.097496	1.879296	1.734070	2.904085	0.3605	0.5543	0.3466	nan	nan
2459986	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	60.693499	6.907526	0.554146	-0.091045	3.952388	2.316870	3.771266	4.783647	0.3826	0.6063	0.3564	nan	nan
2459985	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	61.912742	22.326964	0.502595	-0.484615	4.427797	3.436644	-0.216355	14.873141	0.3203	0.5116	0.3228	nan	nan
2459984	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	58.945225	26.679514	0.574541	-0.570484	6.002112	5.367520	3.379624	2.665035	0.3328	0.4962	0.2689	nan	nan
2459983	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	63.219148	9.431093	0.752740	-0.219672	5.447846	5.765141	5.116805	19.486808	0.3337	0.6028	0.3551	nan	nan
2459982	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	51.690036	9.262623	0.296274	-1.002069	2.573616	3.350638	0.308495	-0.420796	0.4628	0.6146	0.2645	nan	nan
2459981	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	49.559531	13.139176	0.620559	-0.468244	4.924972	6.993290	7.544838	24.596214	0.3258	0.5383	0.3425	nan	nan
2459980	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	48.202099	17.801727	0.338622	-0.864666	4.485468	6.692344	3.158193	1.197523	0.4135	0.5637	0.2578	nan	nan
2459979	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	59.679678	0.695916	0.493422	-0.609698	6.015690	-0.958360	17.305420	0.199391	0.2792	0.6016	0.4454	nan	nan
2459978	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	58.734571	0.736918	0.552355	-0.437296	8.125045	-0.638337	17.634925	0.183354	0.2864	0.5941	0.4364	nan	nan
2459977	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	48.619540	0.883187	0.374890	-0.580480	5.897669	-0.898986	11.326788	-0.277272	0.3279	0.5665	0.3696	nan	nan
2459976	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	59.724987	0.823858	0.573878	-0.507664	4.962520	-1.066140	12.025267	-0.693653	0.2882	0.6098	0.4464	nan	nan

Antenna Season Report Notebook¶

Antenna 77 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 77, Node N06:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 77: 2459996

Antenna 77: 2459995

Antenna 77: 2459994

Antenna 77: 2459991

Antenna 77: 2459990

Antenna 77: 2459989

Antenna 77: 2459988

Antenna 77: 2459987

Antenna 77: 2459986

Antenna 77: 2459985

Antenna 77: 2459984

Antenna 77: 2459983

Antenna 77: 2459982

Antenna 77: 2459981

Antenna 77: 2459980

Antenna 77: 2459979

Antenna 77: 2459978

Antenna 77: 2459977

Antenna 77: 2459976

Antenna Season Report Notebook¶

Antenna 77 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 77, Node N06:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 77: 2459996

Antenna 77: 2459995

Antenna 77: 2459994

Antenna 77: 2459991

Antenna 77: 2459990

Antenna 77: 2459989

Antenna 77: 2459988

Antenna 77: 2459987

Antenna 77: 2459986

Antenna 77: 2459985

Antenna 77: 2459984

Antenna 77: 2459983

Antenna 77: 2459982

Antenna 77: 2459981

Antenna 77: 2459980

Antenna 77: 2459979

Antenna 77: 2459978

Antenna 77: 2459977

Antenna 77: 2459976

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶